Search Results for author: Renshuai Tao

Found 10 papers, 8 papers with code

Data-Independent Operator: A Training-Free Artifact Representation Extractor for Generalizable Deepfake Detection

1 code implementation11 Mar 2024 Chuangchuang Tan, Ping Liu, Renshuai Tao, Huan Liu, Yao Zhao, Baoyuan Wu, Yunchao Wei

Due to its unbias towards both the training and test sources, we define it as Data-Independent Operator (DIO) to achieve appealing improvements on unseen sources.

DeepFake Detection Face Swapping

X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

1 code implementation19 Feb 2023 Aishan Liu, Jun Guo, Jiakai Wang, Siyuan Liang, Renshuai Tao, Wenbo Zhou, Cong Liu, Xianglong Liu, DaCheng Tao

In this paper, we take the first step toward the study of adversarial attacks targeted at X-ray prohibited item detection, and reveal the serious threats posed by such attacks in this safety-critical scenario.

Adversarial Attack

Defensive Patches for Robust Recognition in the Physical World

1 code implementation CVPR 2022 Jiakai Wang, Zixin Yin, Pengfei Hu, Aishan Liu, Renshuai Tao, Haotong Qin, Xianglong Liu, DaCheng Tao

For the generalization against diverse noises, we inject class-specific identifiable patterns into a confined local patch prior, so that defensive patches could preserve more recognizable features towards specific classes, leading models for better recognition under noises.

Exploring Endogenous Shift for Cross-Domain Detection: A Large-Scale Benchmark and Perturbation Suppression Network

1 code implementation CVPR 2022 Renshuai Tao, Hainan Li, Tianbo Wang, Yanlu Wei, Yifu Ding, Bowei Jin, Hongping Zhi, Xianglong Liu, Aishan Liu

To handle the endogenous shift, we further introduce the Perturbation Suppression Network (PSN), motivated by the fact that this shift is mainly caused by two types of perturbations: category-dependent and category-independent ones.

Medical Diagnosis

Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection

1 code implementation ICCV 2021 Renshuai Tao, Yanlu Wei, Xiangjian Jiang, Hainan Li, Haotong Qin, Jiakai Wang, Yuqing Ma, Libo Zhang, Xianglong Liu

In this work, we first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102, 928 common prohibited items of 8 categories.

Learning from Multiple Annotators by Incorporating Instance Features

no code implementations29 Jun 2021 Jingzheng Li, Hailong Sun, Jiyi Li, Zhijun Chen, Renshuai Tao, Yufei Ge

Learning from multiple annotators aims to induce a high-quality classifier from training instances, where each of them is associated with a set of possibly noisy labels provided by multiple annotators under the influence of their varying abilities and own biases.

Diversifying Sample Generation for Accurate Data-Free Quantization

no code implementations CVPR 2021 Xiangguo Zhang, Haotong Qin, Yifu Ding, Ruihao Gong, Qinghua Yan, Renshuai Tao, Yuhang Li, Fengwei Yu, Xianglong Liu

Unfortunately, we find that in practice, the synthetic data identically constrained by BN statistics suffers serious homogenization at both distribution level and sample level and further causes a significant performance drop of the quantized model.

Data Free Quantization Image Classification

Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images

1 code implementation1 Mar 2021 Renshuai Tao, Yanlu Wei, Hainan Li, Aishan Liu, Yifu Ding, Haotong Qin, Xianglong Liu

The images are gathered from an airport and these prohibited items are annotated manually by professional inspectors, which can be used as a benchmark for model training and further facilitate future research.

object-detection Object Detection

Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module

2 code implementations18 Apr 2020 Yanlu Wei, Renshuai Tao, Zhangjie Wu, Yuqing Ma, Libo Zhang, Xianglong Liu

Furthermore, to deal with the occlusion in X-ray images detection, we propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors.

Autonomous Driving object-detection +2

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